An Evaluation of Prediction Accuracy of Machine Learning Algorithms for Arteriosclerosis of the Human Heart

Abstract

The advent of Machine Learning and development in statistics has made it possible to gain crucial insights from immense data obtained from surveys. It has made it easy to interpret huge numbers and made it possible to make predictions with extreme accuracy in a myriad of fields. Healthcare is one such field in which this technology can be extensively applied to make early and precise predictions of diseases based on the medical information of the patient. Arteriosclerosis of the human heart is a major concern worldwide as it is responsible for the majority of deaths. Early signs of arteriosclerosis of human heart prediction based on the health and lifestyle parameters of a patient can prove lifesaving. This research aims to create and train a machine learning model which can predict whether an individual faces a risk of arteriosclerosis. The highest prediction accuracy obtained was 86.8293% by logistic regression.